test_plotly.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# plotly standard imports\n",
    "import plotly.graph_objs as go\n",
    "import plotly.plotly as py\n",
    "\n",
    "# Cufflinks wrapper on plotly\n",
    "import cufflinks as cf\n",
    "\n",
    "# Data science imports\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "# Options for pandas\n",
    "pd.options.display.max_columns = 30\n",
    "\n",
    "# Display all cell outputs\n",
    "from IPython.core.interactiveshell import InteractiveShell\n",
    "InteractiveShell.ast_node_interactivity = 'all'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<script>requirejs.config({paths: { 'plotly': ['https://cdn.plot.ly/plotly-latest.min']},});if(!window.Plotly) {{require(['plotly'],function(plotly) {window.Plotly=plotly;});}}</script>"
      ],
      "text/vnd.plotly.v1+html": [
       "<script>requirejs.config({paths: { 'plotly': ['https://cdn.plot.ly/plotly-latest.min']},});if(!window.Plotly) {{require(['plotly'],function(plotly) {window.Plotly=plotly;});}}</script>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<script>requirejs.config({paths: { 'plotly': ['https://cdn.plot.ly/plotly-latest.min']},});if(!window.Plotly) {{require(['plotly'],function(plotly) {window.Plotly=plotly;});}}</script>"
      ],
      "text/vnd.plotly.v1+html": [
       "<script>requirejs.config({paths: { 'plotly': ['https://cdn.plot.ly/plotly-latest.min']},});if(!window.Plotly) {{require(['plotly'],function(plotly) {window.Plotly=plotly;});}}</script>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from plotly.offline import iplot, init_notebook_mode\n",
    "init_notebook_mode(connected=True)\n",
    "cf.go_offline(connected=True)\n",
    "\n",
    "# Set global theme\n",
    "cf.set_config_file(world_readable=True, theme='pearl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>claps</th>\n",
       "      <th>days_since_publication</th>\n",
       "      <th>fans</th>\n",
       "      <th>link</th>\n",
       "      <th>num_responses</th>\n",
       "      <th>publication</th>\n",
       "      <th>published_date</th>\n",
       "      <th>read_ratio</th>\n",
       "      <th>read_time</th>\n",
       "      <th>reads</th>\n",
       "      <th>started_date</th>\n",
       "      <th>tags</th>\n",
       "      <th>text</th>\n",
       "      <th>title</th>\n",
       "      <th>title_word_count</th>\n",
       "      <th>type</th>\n",
       "      <th>views</th>\n",
       "      <th>word_count</th>\n",
       "      <th>claps_per_word</th>\n",
       "      <th>editing_days</th>\n",
       "      <th>&lt;tag&gt;Education</th>\n",
       "      <th>&lt;tag&gt;Data Science</th>\n",
       "      <th>&lt;tag&gt;Towards Data Science</th>\n",
       "      <th>&lt;tag&gt;Machine Learning</th>\n",
       "      <th>&lt;tag&gt;Python</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>2</td>\n",
       "      <td>574.858594</td>\n",
       "      <td>2</td>\n",
       "      <td>https://medium.com/p/screw-the-environment-but...</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>2017-06-10 14:25:00</td>\n",
       "      <td>41.98</td>\n",
       "      <td>7</td>\n",
       "      <td>68</td>\n",
       "      <td>2017-06-10 14:24:00</td>\n",
       "      <td>[Climate Change, Economics]</td>\n",
       "      <td>Screw the Environment, but Consider Your Walle...</td>\n",
       "      <td>Screw the Environment, but Consider Your Wallet</td>\n",
       "      <td>8</td>\n",
       "      <td>published</td>\n",
       "      <td>162</td>\n",
       "      <td>1859</td>\n",
       "      <td>0.001076</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>18</td>\n",
       "      <td>567.540639</td>\n",
       "      <td>3</td>\n",
       "      <td>https://medium.com/p/the-vanquishing-of-war-pl...</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>2017-06-17 22:02:00</td>\n",
       "      <td>32.93</td>\n",
       "      <td>14</td>\n",
       "      <td>54</td>\n",
       "      <td>2017-06-17 22:02:00</td>\n",
       "      <td>[Climate Change, Humanity, Optimism, History]</td>\n",
       "      <td>The Vanquishing of War, Plague and Famine Part...</td>\n",
       "      <td>The Vanquishing of War, Plague and Famine</td>\n",
       "      <td>8</td>\n",
       "      <td>published</td>\n",
       "      <td>164</td>\n",
       "      <td>3891</td>\n",
       "      <td>0.004626</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>50</td>\n",
       "      <td>554.920762</td>\n",
       "      <td>19</td>\n",
       "      <td>https://medium.com/p/capstone-project-mercedes...</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>2017-06-30 12:55:00</td>\n",
       "      <td>20.19</td>\n",
       "      <td>42</td>\n",
       "      <td>215</td>\n",
       "      <td>2017-06-30 12:00:00</td>\n",
       "      <td>[Machine Learning, Python, Udacity, Kaggle]</td>\n",
       "      <td>Capstone Project: Mercedes-Benz Greener Manufa...</td>\n",
       "      <td>Capstone Project: Mercedes-Benz Greener Manufa...</td>\n",
       "      <td>7</td>\n",
       "      <td>published</td>\n",
       "      <td>1065</td>\n",
       "      <td>12025</td>\n",
       "      <td>0.004158</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>0</td>\n",
       "      <td>554.078160</td>\n",
       "      <td>0</td>\n",
       "      <td>https://medium.com/p/home-of-the-scared-5af0fe...</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>2017-07-01 09:08:00</td>\n",
       "      <td>35.85</td>\n",
       "      <td>9</td>\n",
       "      <td>19</td>\n",
       "      <td>2017-06-30 18:21:00</td>\n",
       "      <td>[Politics, Books, News, Media Criticism]</td>\n",
       "      <td>Home of the Scared A review of A Culture of Fe...</td>\n",
       "      <td>Home of the Scared</td>\n",
       "      <td>4</td>\n",
       "      <td>published</td>\n",
       "      <td>53</td>\n",
       "      <td>2533</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114</th>\n",
       "      <td>0</td>\n",
       "      <td>550.090507</td>\n",
       "      <td>0</td>\n",
       "      <td>https://medium.com/p/the-triumph-of-peace-f485...</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>2017-07-05 08:51:00</td>\n",
       "      <td>8.77</td>\n",
       "      <td>14</td>\n",
       "      <td>5</td>\n",
       "      <td>2017-07-03 20:18:00</td>\n",
       "      <td>[Books, Psychology, History, Humanism]</td>\n",
       "      <td>The Triumph of Peace A review of The Better An...</td>\n",
       "      <td>The Triumph of Peace</td>\n",
       "      <td>4</td>\n",
       "      <td>published</td>\n",
       "      <td>57</td>\n",
       "      <td>3892</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     claps  days_since_publication  fans  \\\n",
       "119      2              574.858594     2   \n",
       "118     18              567.540639     3   \n",
       "121     50              554.920762    19   \n",
       "122      0              554.078160     0   \n",
       "114      0              550.090507     0   \n",
       "\n",
       "                                                  link  num_responses  \\\n",
       "119  https://medium.com/p/screw-the-environment-but...              0   \n",
       "118  https://medium.com/p/the-vanquishing-of-war-pl...              0   \n",
       "121  https://medium.com/p/capstone-project-mercedes...              0   \n",
       "122  https://medium.com/p/home-of-the-scared-5af0fe...              0   \n",
       "114  https://medium.com/p/the-triumph-of-peace-f485...              0   \n",
       "\n",
       "    publication      published_date  read_ratio  read_time  reads  \\\n",
       "119        None 2017-06-10 14:25:00       41.98          7     68   \n",
       "118        None 2017-06-17 22:02:00       32.93         14     54   \n",
       "121        None 2017-06-30 12:55:00       20.19         42    215   \n",
       "122        None 2017-07-01 09:08:00       35.85          9     19   \n",
       "114        None 2017-07-05 08:51:00        8.77         14      5   \n",
       "\n",
       "           started_date                                           tags  \\\n",
       "119 2017-06-10 14:24:00                    [Climate Change, Economics]   \n",
       "118 2017-06-17 22:02:00  [Climate Change, Humanity, Optimism, History]   \n",
       "121 2017-06-30 12:00:00    [Machine Learning, Python, Udacity, Kaggle]   \n",
       "122 2017-06-30 18:21:00       [Politics, Books, News, Media Criticism]   \n",
       "114 2017-07-03 20:18:00         [Books, Psychology, History, Humanism]   \n",
       "\n",
       "                                                  text  \\\n",
       "119  Screw the Environment, but Consider Your Walle...   \n",
       "118  The Vanquishing of War, Plague and Famine Part...   \n",
       "121  Capstone Project: Mercedes-Benz Greener Manufa...   \n",
       "122  Home of the Scared A review of A Culture of Fe...   \n",
       "114  The Triumph of Peace A review of The Better An...   \n",
       "\n",
       "                                                 title  title_word_count  \\\n",
       "119    Screw the Environment, but Consider Your Wallet                 8   \n",
       "118          The Vanquishing of War, Plague and Famine                 8   \n",
       "121  Capstone Project: Mercedes-Benz Greener Manufa...                 7   \n",
       "122                                 Home of the Scared                 4   \n",
       "114                               The Triumph of Peace                 4   \n",
       "\n",
       "          type  views  word_count  claps_per_word  editing_days  \\\n",
       "119  published    162        1859        0.001076             0   \n",
       "118  published    164        3891        0.004626             0   \n",
       "121  published   1065       12025        0.004158             0   \n",
       "122  published     53        2533        0.000000             0   \n",
       "114  published     57        3892        0.000000             1   \n",
       "\n",
       "     <tag>Education  <tag>Data Science  <tag>Towards Data Science  \\\n",
       "119               0                  0                          0   \n",
       "118               0                  0                          0   \n",
       "121               0                  0                          0   \n",
       "122               0                  0                          0   \n",
       "114               0                  0                          0   \n",
       "\n",
       "     <tag>Machine Learning  <tag>Python  \n",
       "119                      0            0  \n",
       "118                      0            0  \n",
       "121                      1            1  \n",
       "122                      0            0  \n",
       "114                      0            0  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = pd.read_parquet('https://github.com/WillKoehrsen/Data-Analysis/blob/master/plotly/data/medium_data_2019_01_06?raw=true')\n",
    "df.head()\n",
    "\n",
    "_ = df['claps'].plot.hist()\n",
    "_ = df.plot(x='fans', y='word_count', kind='scatter')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "data": [
        {
         "histfunc": "count",
         "histnorm": "",
         "marker": {
          "color": "rgba(255, 153, 51, 1.0)",
          "line": {
           "color": "#000000",
           "width": 1.3
          }
         },
         "name": "claps",
         "nbinsx": 30,
         "opacity": 0.8,
         "orientation": "v",
         "type": "histogram",
         "uid": "6bacdf5e-2962-11e9-8db4-001b11c4043c",
         "x": [
          2,
          18,
          50,
          0,
          0,
          0,
          73,
          234,
          2,
          0,
          682,
          58,
          5,
          7,
          87,
          23,
          4,
          113,
          8,
          17,
          8,
          4800,
          2700,
          72,
          123,
          857,
          186,
          11,
          119,
          2000,
          4100,
          59,
          2700,
          77,
          14,
          7000,
          1200,
          275,
          1600,
          1600,
          1000,
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          2900,
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          5300,
          1300,
          1000,
          7900,
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          2200,
          3900,
          6300,
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          1300,
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          2300,
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          707,
          650,
          2700,
          4100,
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          830,
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['claps'].iplot(\n",
    "    kind='hist',\n",
    "    bins=30,\n",
    "    xTitle='claps',\n",
    "    linecolor='black',\n",
    "    yTitle='count',\n",
    "    title='Claps Distribution')"
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   "metadata": {},
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['claps'].iplot(\n",
    "    kind='hist',\n",
    "    bins=30,\n",
    "    xTitle='reads',\n",
    "    linecolor='black',\n",
    "    histnorm='percent',\n",
    "    yTitle='percentage (%)',\n",
    "    title='Reads Distribution in Percent')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "from plotly.offline import iplot, init_notebook_mode\n",
    "import plotly.graph_objs as go\n",
    "import plotly.io as pio\n",
    "import plotly as pl\n",
    "\n",
    "import os\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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      "text/vnd.plotly.v1+html": [
       "<script>requirejs.config({paths: { 'plotly': ['https://cdn.plot.ly/plotly-latest.min']},});if(!window.Plotly) {{require(['plotly'],function(plotly) {window.Plotly=plotly;});}}</script>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "init_notebook_mode(connected=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    'mode': 'markers',\n",
       "    'uid': '6e92c9a2-2a2d-11e9-9933-001b11c4043c',\n",
       "    'x': array([0.15048979, 0.27687647, 0.02537818, 0.14657075, 0.2142371 , 0.39005985,\n",
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "N = 100\n",
    "x = np.random.rand(N)\n",
    "y = np.random.rand(N)\n",
    "colors = np.random.rand(N)\n",
    "sz = np.random.rand(N)*30\n",
    "\n",
    "fig = go.Figure()\n",
    "fig.add_scatter(x=x,\n",
    "                y=y,\n",
    "                mode='markers',\n",
    "                marker={'size': sz,\n",
    "                        'color': colors,\n",
    "                        'opacity': 0.6,\n",
    "                        'colorscale': 'Viridis'\n",
    "                       });\n",
    "iplot(fig)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "if not os.path.exists('images'):\n",
    "    os.mkdir('images')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "\nThe orca executable is required to export figures as static images,\nbut it could not be found on the system path.\n\nSearched for executable 'F:\\YandexDisk\\3.Programing\\balovstvo&checking\\' on the following path:\n    C:\\Windows\\system32\n    C:\\Windows\n    C:\\Windows\\System32\\Wbem\n    C:\\Windows\\System32\\WindowsPowerShell\\v1.0\\\n    C:\\ProgramData\\AutomationBox\\MouseRobot2\\Application\n    C:\\ProgramData\\AutomationBox\\MouseRobot2\\Application\\tools\n    C:\\Program Files (x86)\\NVIDIA Corporation\\PhysX\\Common\n    C:\\ProgramData\\chocolatey\\bin\n    C:\\Program Files\\dotnet\\\n    C:\\Program Files (x86)\\Windows Kits\\8.1\\Windows Performance Toolkit\\\n    C:\\tools\\mingw64\\bin\n    C:\\Windows\\SysWOW64\n    C:\\Program Files (x86)\\vim\\vim80\n    C:\\Program Files\\Git\\cmd\n    C:\\Users\\Professional\\AppData\\Local\\Programs\\Python\\Python35\\Scripts\\\n    C:\\Users\\Professional\\AppData\\Local\\Programs\\Python\\Python35\\\n    C:\\Go\\bin\n    C:\\Program Files\\gnuplot\\bin\n    C:\\Program Files\\PostgreSQL\\10\\bin\n    C:\\Program Files\\PostgreSQL\\10\\lib\n    C:\\Program Files (x86)\\Wolfram Research\\WolframScript\\\n    C:\\Users\\Professional\\AppData\\Local\\Programs\\Python\\Python35\\Scripts\\\n    C:\\Users\\Professional\\AppData\\Local\\Programs\\Python\\Python35\\\n    C:\\Users\\Professional\\AppData\\Local\\Microsoft\\WindowsApps\n    C:\\tools\\mingw64\\bin\n    C:\\Users\\Professional\\go\\bin\n    C:\\Users\\Professional\\AppData\\Local\\Programs\\MiKTeX 2.9\\miktex\\bin\\x64\\\n    c:\\users\\professional\\appdata\\local\\programs\\python\\python35\\lib\\site-packages\\pywin32_system32\n    c:\\users\\professional\\appdata\\local\\programs\\python\\python35\\lib\\site-packages\\pywin32_system32\n    c:\\users\\professional\\appdata\\local\\programs\\python\\python35\\lib\\site-packages\\numpy\\.libs\n    c:\\users\\professional\\appdata\\local\\programs\\python\\python35\\lib\\site-packages\\scipy\\extra-dll\n\nIf you haven't installed orca yet, you can do so using conda as follows:\n\n    $ conda install -c plotly plotly-orca\n\nAlternatively, see other installation methods in the orca project README at\nhttps://github.com/plotly/orca.\n\nAfter installation is complete, no further configuration should be needed. \n\nIf you have installed orca, then for some reason plotly.py was unable to\nlocate it. In this case, set the `plotly.io.orca.config.executable`\nproperty to the full path of your orca executable. For example:\n\n    >>> plotly.io.orca.config.executable = '/path/to/orca'\n\nAfter updating this executable property, try the export operation again.\nIf it is successful then you may want to save this configuration so that it\nwill be applied automatically in future sessions. You can do this as follows:\n\n    >>> plotly.io.orca.config.save() \n\nIf you're still having trouble, feel free to ask for help on the forums at\nhttps://community.plot.ly/c/api/python\n",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-21-557904b820fe>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mpio\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwrite_image\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'images\\fig1.png'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32mc:\\users\\professional\\appdata\\local\\programs\\python\\python35\\lib\\site-packages\\plotly\\io\\_orca.py\u001b[0m in \u001b[0;36mwrite_image\u001b[1;34m(fig, file, format, scale, width, height, validate)\u001b[0m\n\u001b[0;32m   1484\u001b[0m                         \u001b[0mwidth\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mwidth\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1485\u001b[0m                         \u001b[0mheight\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mheight\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1486\u001b[1;33m                         validate=validate)\n\u001b[0m\u001b[0;32m   1487\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1488\u001b[0m     \u001b[1;31m# Open file\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\professional\\appdata\\local\\programs\\python\\python35\\lib\\site-packages\\plotly\\io\\_orca.py\u001b[0m in \u001b[0;36mto_image\u001b[1;34m(fig, format, width, height, scale, validate)\u001b[0m\n\u001b[0;32m   1266\u001b[0m     \u001b[1;31m# Make sure orca sever is running\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1267\u001b[0m     \u001b[1;31m# -------------------------------\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1268\u001b[1;33m     \u001b[0mensure_server\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1269\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1270\u001b[0m     \u001b[1;31m# Handle defaults\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\professional\\appdata\\local\\programs\\python\\python35\\lib\\site-packages\\plotly\\io\\_orca.py\u001b[0m in \u001b[0;36mensure_server\u001b[1;34m()\u001b[0m\n\u001b[0;32m   1137\u001b[0m     \u001b[1;31m# Validate orca executable\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1138\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mstatus\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstate\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m'unvalidated'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1139\u001b[1;33m         \u001b[0mvalidate_executable\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1140\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1141\u001b[0m     \u001b[1;31m# Acquire lock to make sure that we keep the properties of orca_state\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\professional\\appdata\\local\\programs\\python\\python35\\lib\\site-packages\\plotly\\io\\_orca.py\u001b[0m in \u001b[0;36mvalidate_executable\u001b[1;34m()\u001b[0m\n\u001b[0;32m    915\u001b[0m             \u001b[0mexecutable\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mconfig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mexecutable\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    916\u001b[0m             \u001b[0mformatted_path\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mformatted_path\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 917\u001b[1;33m             instructions=install_location_instructions))\n\u001b[0m\u001b[0;32m    918\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    919\u001b[0m     \u001b[1;31m# Clear NODE_OPTIONS environment variable\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: \nThe orca executable is required to export figures as static images,\nbut it could not be found on the system path.\n\nSearched for executable 'F:\\YandexDisk\\3.Programing\\balovstvo&checking\\' on the following path:\n    C:\\Windows\\system32\n    C:\\Windows\n    C:\\Windows\\System32\\Wbem\n    C:\\Windows\\System32\\WindowsPowerShell\\v1.0\\\n    C:\\ProgramData\\AutomationBox\\MouseRobot2\\Application\n    C:\\ProgramData\\AutomationBox\\MouseRobot2\\Application\\tools\n    C:\\Program Files (x86)\\NVIDIA Corporation\\PhysX\\Common\n    C:\\ProgramData\\chocolatey\\bin\n    C:\\Program Files\\dotnet\\\n    C:\\Program Files (x86)\\Windows Kits\\8.1\\Windows Performance Toolkit\\\n    C:\\tools\\mingw64\\bin\n    C:\\Windows\\SysWOW64\n    C:\\Program Files (x86)\\vim\\vim80\n    C:\\Program Files\\Git\\cmd\n    C:\\Users\\Professional\\AppData\\Local\\Programs\\Python\\Python35\\Scripts\\\n    C:\\Users\\Professional\\AppData\\Local\\Programs\\Python\\Python35\\\n    C:\\Go\\bin\n    C:\\Program Files\\gnuplot\\bin\n    C:\\Program Files\\PostgreSQL\\10\\bin\n    C:\\Program Files\\PostgreSQL\\10\\lib\n    C:\\Program Files (x86)\\Wolfram Research\\WolframScript\\\n    C:\\Users\\Professional\\AppData\\Local\\Programs\\Python\\Python35\\Scripts\\\n    C:\\Users\\Professional\\AppData\\Local\\Programs\\Python\\Python35\\\n    C:\\Users\\Professional\\AppData\\Local\\Microsoft\\WindowsApps\n    C:\\tools\\mingw64\\bin\n    C:\\Users\\Professional\\go\\bin\n    C:\\Users\\Professional\\AppData\\Local\\Programs\\MiKTeX 2.9\\miktex\\bin\\x64\\\n    c:\\users\\professional\\appdata\\local\\programs\\python\\python35\\lib\\site-packages\\pywin32_system32\n    c:\\users\\professional\\appdata\\local\\programs\\python\\python35\\lib\\site-packages\\pywin32_system32\n    c:\\users\\professional\\appdata\\local\\programs\\python\\python35\\lib\\site-packages\\numpy\\.libs\n    c:\\users\\professional\\appdata\\local\\programs\\python\\python35\\lib\\site-packages\\scipy\\extra-dll\n\nIf you haven't installed orca yet, you can do so using conda as follows:\n\n    $ conda install -c plotly plotly-orca\n\nAlternatively, see other installation methods in the orca project README at\nhttps://github.com/plotly/orca.\n\nAfter installation is complete, no further configuration should be needed. \n\nIf you have installed orca, then for some reason plotly.py was unable to\nlocate it. In this case, set the `plotly.io.orca.config.executable`\nproperty to the full path of your orca executable. For example:\n\n    >>> plotly.io.orca.config.executable = '/path/to/orca'\n\nAfter updating this executable property, try the export operation again.\nIf it is successful then you may want to save this configuration so that it\nwill be applied automatically in future sessions. You can do this as follows:\n\n    >>> plotly.io.orca.config.save() \n\nIf you're still having trouble, feel free to ask for help on the forums at\nhttps://community.plot.ly/c/api/python\n"
     ]
    }
   ],
   "source": [
    "pio.write_image(fig, 'images\\fig1.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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